code: def sampler(self) -> LinearEllipticalSliceSampler:
      if self._sampler is None:
          eye = torch.eye(
              self.scale_tril.shape[-1],
              dtype=self.scale_tril.dtype,
              device=self.scale_tril.device,
          )
  
          A = torch.concat([-eye, eye])
          b = torch.concat(
              [
                  self.loc - self.bounds[..., 0],
                  self.bounds[..., 1] - self.loc,
              ],
              dim=-1,
          ).unsqueeze(-1)
  
          self._sampler = LinearEllipticalSliceSampler(
              inequality_constraints=(A, b),
              covariance_root=self.scale_tril,
          )
      return self._sampler
  
file_path: botorch/botorch/utils/probability/truncated_multivariate_normal.py
imports: from __future__ import annotations
  from typing import Optional, Sequence
  import torch
  from botorch.utils.probability.lin_ess import LinearEllipticalSliceSampler
  from botorch.utils.probability.mvnxpb import MVNXPB
  from botorch.utils.probability.utils import get_constants_like
  from torch import Tensor
  from torch.distributions.multivariate_normal import MultivariateNormal
label_desc: Use image node_id 6 for calling the TruncatedMultivariateNormal obj's underlying member method code with example usage: obj.sampler() and returns: self